The Global Burden of Absenteeism Related to COVID-19 Vaccine Side Effects Among Healthcare Workers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A rise in absenteeism among healthcare workers (HCWs) was recorded during the COVID-19 pandemic, mostly attributed to SARS-CoV-2 infections. However, evidence suggests that COVID-19 vaccine-related side effects may have also contributed to absenteeism during this period. This study aimed to synthesize the evidence on the prevalence of absenteeism related to COVID-19 vaccine side effects among HCWs. Methods: The inclusion criteria for this review were original quantitative studies of any design, written in English, that addressed absenteeism related to the side effects of COVID-19 vaccines among HCWs. Four databases (PubMed, Scopus, Embase, and the Web of Science) were searched for eligible articles on 7 June 2024. The risk of bias was assessed using the Newcastle–Ottawa scale. Narrative synthesis and a meta-analysis were used to synthesize the evidence. Results: Nineteen observational studies with 96,786 participants were included. The pooled prevalence of absenteeism related to COVID-19 vaccine side effects was 17% (95% CI: 13–20%), while 83% (95% CI: 80–87%) of the vaccination events did not lead in any absenteeism. Study design, sex, vaccination dose, region, and vaccine type were identified as significant sources of heterogeneity. Conclusions: A non-negligible proportion of HCWs were absent from work after reporting side effects of the COVID-19 vaccine. Various demographic factors should be considered in future vaccination schedules for HCWs to potentially decrease the burden of absenteeism related to vaccine side effects. As most studies included self-reported questionnaire data, our results may be limited due to a recall bias. Other: The protocol of the study was preregistered in the PROSPERO database (CRD42024552517).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".